Just Prompt

Instructions

Implementation Reference

• src/just_prompt/molecules/prompt_from_file.py:14-44 (handler)

  Core handler function that reads the content from the specified absolute file path, validates the file, and delegates to the prompt function with multiple models.

  def prompt_from_file(abs_file_path: str, models_prefixed_by_provider: List[str] = None) -> List[str]:
      """
      Read text from a file and send it as a prompt to multiple models.
      
      Args:
          abs_file_path: Absolute path to the text file (must be an absolute path, not relative)
          models_prefixed_by_provider: List of model strings in format "provider:model"
                                      If None, uses the DEFAULT_MODELS environment variable
          
      Returns:
          List of responses from the models
      """
      file_path = Path(abs_file_path)
      
      # Validate file
      if not file_path.exists():
          raise FileNotFoundError(f"File not found: {abs_file_path}")
      
      if not file_path.is_file():
          raise ValueError(f"Not a file: {abs_file_path}")
      
      # Read file content
      try:
          with open(file_path, 'r', encoding='utf-8') as f:
              text = f.read()
      except Exception as e:
          logger.error(f"Error reading file {abs_file_path}: {e}")
          raise ValueError(f"Error reading file: {str(e)}")
      
      # Send prompt with file content
      return prompt(text, models_prefixed_by_provider)

• src/just_prompt/server.py:52-57 (schema)

  Pydantic input schema for validating the tool arguments: absolute file path and optional list of models.

  class PromptFromFileSchema(BaseModel):
      abs_file_path: str = Field(..., description="Absolute path to the file containing the prompt (must be an absolute path, not relative)")
      models_prefixed_by_provider: Optional[List[str]] = Field(
          None, 
          description="List of models with provider prefixes (e.g., 'openai:gpt-4o' or 'o:gpt-4o'). If not provided, uses default models."
      )

• src/just_prompt/server.py:127-131 (registration)

  MCP Tool registration in the list_tools() method, specifying name, description, and input schema.

  Tool(
      name=JustPromptTools.PROMPT_FROM_FILE,
      description="Send a prompt from a file to multiple LLM models. IMPORTANT: You MUST provide an absolute file path (e.g., /path/to/file or C:\\path\\to\\file), not a relative path.",
      inputSchema=PromptFromFileSchema.schema(),
  ),

• src/just_prompt/server.py:173-184 (handler)

  Dispatch handler in the MCP call_tool method that invokes the prompt_from_file function and formats the responses as TextContent.

  elif name == JustPromptTools.PROMPT_FROM_FILE:
      models_to_use = arguments.get("models_prefixed_by_provider")
      responses = prompt_from_file(arguments["abs_file_path"], models_to_use)
      
      # Get the model names that were actually used
      models_used = models_to_use if models_to_use else [model.strip() for model in os.environ.get("DEFAULT_MODELS", DEFAULT_MODEL).split(",")]
      
      return [TextContent(
          type="text",
          text="\n".join([f"Model: {models_used[i]}\nResponse: {resp}" 
                        for i, resp in enumerate(responses)])
      )]

• src/just_prompt/server.py:38-38 (registration)

  Tool name constant definition in JustPromptTools enum-like class.

  PROMPT_FROM_FILE = "prompt_from_file"